Some early deep learning approaches transformed point clouds into 3D voxel structure and convolve it with standard 3D kernels. VoxNet [1]} and subsequent works [2]}, [3]}, [4]} discretized point cloud into a 3D binary occupancy grid. The occupancy grid is fed to a CNN for object proposal and classification. These voxel-based methods suffered from high memory consumption due to the waste of computation on empty spaces. OctNet[5]}, [6]} proposed adaptive representation using octree structure to reduce memory consumption. Recent researches [7]}, [8]} introduced approaches to process high dimensional data and apply sparse convolution only on non-empty voxels. In general, volumetric methods preserve neighborhood information of point clouds, enable regular 3D CNN applications, but suffer from significant discretization artifacts.
